{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "## Linear Regression——线性模型"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "cc7a37b631644612"
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2025-05-06T13:24:55.702760Z",
     "start_time": "2025-05-06T13:24:55.696776Z"
    }
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import pickle\n",
    "import os\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import accuracy_score, precision_recall_fscore_support, confusion_matrix, classification_report\n",
    "data_dir = r'D:\\Machine_learning\\jiqixuexi\\cifar-10-python\\cifar-10-batches-py'"
   ]
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "# 加载CIFAR-10数据集\n",
    "def load_cifar10_data(data_dir):\n",
    "    # 加载训练数据\n",
    "    train_data = []\n",
    "    train_labels = []\n",
    "    for i in range(1, 6):\n",
    "        batch_path = os.path.join(data_dir, f'data_batch_{i}')\n",
    "        with open(batch_path, 'rb') as file:\n",
    "            batch = pickle.load(file, encoding='latin1')\n",
    "            train_data.append(batch['data'])\n",
    "            train_labels.extend(batch['labels'])\n",
    "    train_data = np.array(train_data).reshape(50000, 3, 32, 32).transpose(0, 2, 3, 1)\n",
    "    train_labels = np.array(train_labels)\n",
    "\n",
    "    # 加载测试数据\n",
    "    test_batch_path = os.path.join(data_dir, 'test_batch')\n",
    "    with open(test_batch_path, 'rb') as file:\n",
    "        test_batch = pickle.load(file, encoding='latin1')\n",
    "    test_data = test_batch['data'].reshape(10000, 3, 32, 32).transpose(0, 2, 3, 1)\n",
    "    test_labels = np.array(test_batch['labels'])\n",
    "\n",
    "    return (train_data, train_labels), (test_data, test_labels)\n",
    "\n",
    "# 调用函数加载数据\n",
    "(train_data, train_labels), (test_data, test_labels) = load_cifar10_data(data_dir)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-05-06T13:24:57.682133Z",
     "start_time": "2025-05-06T13:24:57.481430Z"
    }
   },
   "id": "a015ae06726a547a",
   "execution_count": 7
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 数据预处理"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "35eeb9fefcd9bc66"
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "# 数据预处理\n",
    "train_data = train_data.astype('float32') / 255.0\n",
    "test_data = test_data.astype('float32') / 255.0\n",
    "\n",
    "# 将图像数据展平为一维向量（32x32x3=3072）\n",
    "train_data = train_data.reshape(-1, 32*32*3)\n",
    "test_data = test_data.reshape(-1, 32*32*3)\n",
    "\n",
    "# 将标签转换为整数张量\n",
    "train_labels = tf.convert_to_tensor(train_labels, dtype=tf.int32)\n",
    "test_labels = tf.convert_to_tensor(test_labels, dtype=tf.int32)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-05-06T13:25:01.397462Z",
     "start_time": "2025-05-06T13:25:00.379960Z"
    }
   },
   "id": "52860ed51fb4c7b9",
   "execution_count": 8
  },
  {
   "cell_type": "markdown",
   "source": [],
   "metadata": {
    "collapsed": false
   },
   "id": "70ad06653c3788c9"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "\u001B[1m500/500\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 1ms/step - accuracy: 0.2758 - loss: 2.0262\n",
      "Epoch 2/10\n",
      "\u001B[1m500/500\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 1ms/step - accuracy: 0.3647 - loss: 1.8295\n",
      "Epoch 3/10\n",
      "\u001B[1m500/500\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 1ms/step - accuracy: 0.3711 - loss: 1.8042\n",
      "Epoch 4/10\n",
      "\u001B[1m500/500\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 1ms/step - accuracy: 0.3764 - loss: 1.8048\n",
      "Epoch 5/10\n",
      "\u001B[1m500/500\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 1ms/step - accuracy: 0.3824 - loss: 1.7743\n",
      "Epoch 6/10\n",
      "\u001B[1m500/500\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 1ms/step - accuracy: 0.3889 - loss: 1.7742\n",
      "Epoch 7/10\n",
      "\u001B[1m500/500\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 1ms/step - accuracy: 0.3872 - loss: 1.7681\n",
      "Epoch 8/10\n",
      "\u001B[1m500/500\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 1ms/step - accuracy: 0.4018 - loss: 1.7376\n",
      "Epoch 9/10\n",
      "\u001B[1m500/500\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 1ms/step - accuracy: 0.3943 - loss: 1.7488\n",
      "Epoch 10/10\n",
      "\u001B[1m500/500\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 1ms/step - accuracy: 0.3984 - loss: 1.7441\n",
      "\u001B[1m100/100\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 717us/step\n"
     ]
    }
   ],
   "source": [
    "# 创建模型\n",
    "model = tf.keras.Sequential([\n",
    "    tf.keras.layers.Dense(10, activation='softmax')\n",
    "])\n",
    "# 编译模型\n",
    "model.compile(optimizer='adam',\n",
    "              loss='sparse_categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "# 转换为 TensorFlow 数据集格式\n",
    "train_dataset = tf.data.Dataset.from_tensor_slices((train_data, train_labels))\n",
    "test_dataset = tf.data.Dataset.from_tensor_slices((test_data, test_labels))\n",
    "# 设置批大小\n",
    "batch_size = 100\n",
    "train_dataset = train_dataset.shuffle(50000).batch(batch_size)\n",
    "test_dataset = test_dataset.batch(batch_size)\n",
    "# 模型训练\n",
    "model.fit(train_dataset, epochs=10)\n",
    "# 模型预测\n",
    "y_pred = model.predict(test_dataset)\n",
    "y_pred_classes = np.argmax(y_pred, axis=1)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-05-06T13:25:13.351554Z",
     "start_time": "2025-05-06T13:25:05.774654Z"
    }
   },
   "id": "d05b4ae04f13da4c",
   "execution_count": 9
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.3749\n",
      "Precision: 0.4182482992813174\n",
      "Recall: 0.3749\n",
      "F1-score: 0.3496097365497004\n",
      "Confusion Matrix:\n",
      " [[652 101  47  13   8   7  46  33  90   3]\n",
      " [ 97 685  18  19  10  24  60  32  39  16]\n",
      " [170  72 283  50  61  44 241  52  24   3]\n",
      " [110 140 113 201  24 121 236  32  20   3]\n",
      " [109  60 168  50 190  39 288  79  15   2]\n",
      " [105  99 123 123  45 254 179  44  28   0]\n",
      " [ 29  77  68  69  42  36 648  19  10   2]\n",
      " [115 103  98  48  53  50 103 398  21  11]\n",
      " [328 198  17  22   3  34  30  12 348   8]\n",
      " [159 484  13  19  10  17  78  57  73  90]]\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.35      0.65      0.45      1000\n",
      "           1       0.34      0.69      0.45      1000\n",
      "           2       0.30      0.28      0.29      1000\n",
      "           3       0.33      0.20      0.25      1000\n",
      "           4       0.43      0.19      0.26      1000\n",
      "           5       0.41      0.25      0.31      1000\n",
      "           6       0.34      0.65      0.45      1000\n",
      "           7       0.53      0.40      0.45      1000\n",
      "           8       0.52      0.35      0.42      1000\n",
      "           9       0.65      0.09      0.16      1000\n",
      "\n",
      "    accuracy                           0.37     10000\n",
      "   macro avg       0.42      0.37      0.35     10000\n",
      "weighted avg       0.42      0.37      0.35     10000\n"
     ]
    }
   ],
   "source": [
    "# 模型评估\n",
    "accuracy = accuracy_score(test_labels, y_pred_classes)\n",
    "precision, recall, f1, _ = precision_recall_fscore_support(test_labels, y_pred_classes, average='weighted')\n",
    "conf_matrix = confusion_matrix(test_labels, y_pred_classes)\n",
    "class_report = classification_report(test_labels, y_pred_classes)\n",
    "\n",
    "print(\"Accuracy:\", accuracy)\n",
    "print(\"Precision:\", precision)\n",
    "print(\"Recall:\", recall)\n",
    "print(\"F1-score:\", f1)\n",
    "print(\"Confusion Matrix:\\n\", conf_matrix)\n",
    "print(\"Classification Report:\\n\", class_report)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-05-06T13:25:18.008842Z",
     "start_time": "2025-05-06T13:25:17.973391Z"
    }
   },
   "id": "ff15b5e56983414a",
   "execution_count": 10
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1200x800 with 2 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制混淆矩阵热力图\n",
    "plt.figure(figsize=(12, 8))\n",
    "sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues')\n",
    "plt.title(\"Linear Model - Confusion Matrix (CIFAR-10)\")\n",
    "plt.xlabel(\"Predicted Label\")\n",
    "plt.ylabel(\"True Label\")\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-05-06T13:28:08.531976Z",
     "start_time": "2025-05-06T13:28:07.787365Z"
    }
   },
   "id": "52997c1f12afe341",
   "execution_count": 11
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   },
   "id": "6b710891ec54ab34"
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
